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20242026
most citedCross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges

4 citations · 4 across the 8 of their papers we have counts for

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cs.CL2026

FineDialFact: A benchmark for Fine-grained Dialogue Fact Verification

Xiangyan Chen, Yufeng Li, Yujian Gan +2

Large language models are known to produce hallucinations - factually incorrect or fabricated information - which poses significant challenges for many natural language processing…

cs.CL2026

Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content

Shaz Furniturewala, Arkaitz Zubiaga

The volume of machine-generated content online has grown dramatically due to the widespread use of Large Language Models (LLMs), leading to new challenges for content moderation sy…

cs.CL2026

BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking

Hyunkyung Park, Arkaitz Zubiaga

Automated fact-checking in dialogue involves multi-turn conversations where colloquial language is frequent yet understudied. To address this gap, we propose a conservative rewrite…

cs.CL2026

Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering

Rrubaa Panchendrarajan, Arkaitz Zubiaga

Recurrent claims present a major challenge for automated fact-checking systems designed to combat misinformation, especially in multilingual settings. While tasks such as claim mat…

cs.CL2026

ContextClaim: A Context-Driven Paradigm for Verifiable Claim Detection

Yufeng Li, Rrubaa Panchendrarajan, Arkaitz Zubiaga

Automated fact-checking pipelines typically begin with a filtering stage that decides which claims are worth verifying, given that the later evidence retrieval and verification com…

cs.CL20264 cited

Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges

Aiqi Jiang, Arkaitz Zubiaga

The growing prevalence and rapid evolution of offensive language in social media amplify the complexities of detection, particularly highlighting the challenges in identifying such…